Overview

Dataset statistics

Number of variables14
Number of observations320280
Missing cells48827
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 MiB
Average record size in memory112.0 B

Variable types

Categorical4
DateTime1
Numeric9

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
TYPERENDE_DIAGNOSE_CD has a high cardinality: 1899 distinct valuesHigh cardinality
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 48827 (15.2%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.19692021)Skewed

Reproduction

Analysis started2023-03-08 14:29:41.465602
Analysis finished2023-03-08 14:30:01.855412
Duration20.39 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1.0
320280 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters960840
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 320280
100.0%

Length

2023-03-08T14:30:01.928092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-08T14:30:02.055215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 320280
100.0%

Most occurring characters

ValueCountFrequency (%)
1 320280
33.3%
. 320280
33.3%
0 320280
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 640560
66.7%
Other Punctuation 320280
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 320280
50.0%
0 320280
50.0%
Other Punctuation
ValueCountFrequency (%)
. 320280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 960840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 320280
33.3%
. 320280
33.3%
0 320280
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 960840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 320280
33.3%
. 320280
33.3%
0 320280
33.3%

DATUM_BESTAND
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2023-02-03
320280 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3202800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-03
2nd row2023-02-03
3rd row2023-02-03
4th row2023-02-03
5th row2023-02-03

Common Values

ValueCountFrequency (%)
2023-02-03 320280
100.0%

Length

2023-03-08T14:30:02.145870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-08T14:30:02.261199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-03 320280
100.0%

Most occurring characters

ValueCountFrequency (%)
2 960840
30.0%
0 960840
30.0%
3 640560
20.0%
- 640560
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2562240
80.0%
Dash Punctuation 640560
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 960840
37.5%
0 960840
37.5%
3 640560
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 640560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3202800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 960840
30.0%
0 960840
30.0%
3 640560
20.0%
- 640560
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3202800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 960840
30.0%
0 960840
30.0%
3 640560
20.0%
- 640560
20.0%

PEILDATUM
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2023-02-01
320280 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3202800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-01
2nd row2023-02-01
3rd row2023-02-01
4th row2023-02-01
5th row2023-02-01

Common Values

ValueCountFrequency (%)
2023-02-01 320280
100.0%

Length

2023-03-08T14:30:02.354546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-08T14:30:02.471896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-01 320280
100.0%

Most occurring characters

ValueCountFrequency (%)
2 960840
30.0%
0 960840
30.0%
- 640560
20.0%
3 320280
 
10.0%
1 320280
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2562240
80.0%
Dash Punctuation 640560
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 960840
37.5%
0 960840
37.5%
3 320280
 
12.5%
1 320280
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 640560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3202800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 960840
30.0%
0 960840
30.0%
- 640560
20.0%
3 320280
 
10.0%
1 320280
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3202800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 960840
30.0%
0 960840
30.0%
- 640560
20.0%
3 320280
 
10.0%
1 320280
 
10.0%

JAAR
Date

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Minimum2012-01-01 00:00:00
Maximum2022-01-01 00:00:00
2023-03-08T14:30:02.556985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:30:02.775022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.71961
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:02.895769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile335
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation987.35022
Coefficient of variation (CV)2.2556682
Kurtosis61.222425
Mean437.71961
Median Absolute Deviation (MAD)8
Skewness7.9457087
Sum1.4019284 × 108
Variance974860.45
MonotonicityNot monotonic
2023-03-08T14:30:03.026813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 45280
14.1%
313 41468
12.9%
303 36880
11.5%
330 25328
 
7.9%
316 21805
 
6.8%
308 17276
 
5.4%
306 13436
 
4.2%
324 13228
 
4.1%
301 12828
 
4.0%
304 10397
 
3.2%
Other values (18) 82354
25.7%
ValueCountFrequency (%)
301 12828
 
4.0%
302 7015
 
2.2%
303 36880
11.5%
304 10397
 
3.2%
305 45280
14.1%
306 13436
 
4.2%
307 5583
 
1.7%
308 17276
 
5.4%
310 3499
 
1.1%
313 41468
12.9%
ValueCountFrequency (%)
8418 4260
 
1.3%
8416 560
 
0.2%
1900 210
 
0.1%
390 864
 
0.3%
389 3382
 
1.1%
362 4311
 
1.3%
361 2303
 
0.7%
335 3236
 
1.0%
330 25328
7.9%
329 834
 
0.3%
Distinct1899
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
101
 
1355
402
 
1309
403
 
1282
301
 
1282
201
 
1204
Other values (1894)
313848 

Length

Max length4
Median length3
Mean length3.352707
Min length2

Characters and Unicode

Total characters1073805
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
101 1355
 
0.4%
402 1309
 
0.4%
403 1282
 
0.4%
301 1282
 
0.4%
201 1204
 
0.4%
203 1197
 
0.4%
401 1069
 
0.3%
404 1060
 
0.3%
802 1045
 
0.3%
409 1035
 
0.3%
Other values (1889) 308442
96.3%

Length

2023-03-08T14:30:03.217097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
101 1355
 
0.4%
402 1309
 
0.4%
403 1282
 
0.4%
301 1282
 
0.4%
201 1204
 
0.4%
203 1197
 
0.4%
401 1069
 
0.3%
404 1060
 
0.3%
802 1045
 
0.3%
409 1035
 
0.3%
Other values (1889) 308442
96.3%

Most occurring characters

ValueCountFrequency (%)
1 205471
19.1%
0 196833
18.3%
2 142360
13.3%
3 116293
10.8%
5 82848
7.7%
9 77363
 
7.2%
4 76135
 
7.1%
7 63249
 
5.9%
6 56160
 
5.2%
8 46242
 
4.3%
Other values (15) 10851
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1062954
99.0%
Uppercase Letter 10851
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2022
18.6%
M 1835
16.9%
B 1302
12.0%
E 913
8.4%
Z 906
8.3%
D 728
 
6.7%
A 705
 
6.5%
F 676
 
6.2%
C 359
 
3.3%
K 350
 
3.2%
Other values (5) 1055
9.7%
Decimal Number
ValueCountFrequency (%)
1 205471
19.3%
0 196833
18.5%
2 142360
13.4%
3 116293
10.9%
5 82848
7.8%
9 77363
 
7.3%
4 76135
 
7.2%
7 63249
 
6.0%
6 56160
 
5.3%
8 46242
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1062954
99.0%
Latin 10851
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2022
18.6%
M 1835
16.9%
B 1302
12.0%
E 913
8.4%
Z 906
8.3%
D 728
 
6.7%
A 705
 
6.5%
F 676
 
6.2%
C 359
 
3.3%
K 350
 
3.2%
Other values (5) 1055
9.7%
Common
ValueCountFrequency (%)
1 205471
19.3%
0 196833
18.5%
2 142360
13.4%
3 116293
10.9%
5 82848
7.8%
9 77363
 
7.3%
4 76135
 
7.2%
7 63249
 
6.0%
6 56160
 
5.3%
8 46242
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1073805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 205471
19.1%
0 196833
18.3%
2 142360
13.3%
3 116293
10.8%
5 82848
7.7%
9 77363
 
7.2%
4 76135
 
7.1%
7 63249
 
5.9%
6 56160
 
5.2%
8 46242
 
4.3%
Other values (15) 10851
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6017
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4209996 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:03.375440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199899010
median1.49899 × 108
Q39.90004 × 108
95-th percentile9.9051604 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9010499 × 108

Descriptive statistics

Standard deviation4.2928588 × 108
Coefficient of variation (CV)0.97101543
Kurtosis-1.7434152
Mean4.4209996 × 108
Median Absolute Deviation (MAD)1.1999999 × 108
Skewness0.46129299
Sum1.4159578 × 1014
Variance1.8428637 × 1017
MonotonicityNot monotonic
2023-03-08T14:30:03.533862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2322
 
0.7%
990004007 2290
 
0.7%
990003004 2242
 
0.7%
990004006 1868
 
0.6%
990356076 1691
 
0.5%
990356073 1566
 
0.5%
131999228 1489
 
0.5%
131999164 1475
 
0.5%
990003007 1464
 
0.5%
131999194 1352
 
0.4%
Other values (6007) 302521
94.5%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 11
< 0.1%
10501004 11
< 0.1%
10501005 11
< 0.1%
10501007 3
 
< 0.1%
10501008 11
< 0.1%
10501010 11
< 0.1%
10501011 3
 
< 0.1%
11101002 10
< 0.1%
11101003 11
< 0.1%
ValueCountFrequency (%)
998418081 162
0.1%
998418080 145
< 0.1%
998418079 38
 
< 0.1%
998418077 8
 
< 0.1%
998418076 8
 
< 0.1%
998418075 7
 
< 0.1%
998418074 214
0.1%
998418073 215
0.1%
998418072 8
 
< 0.1%
998418071 8
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

Distinct10093
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean518.9254
Minimum1
Maximum165142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:03.688747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3105
95-th percentile1770
Maximum165142
Range165141
Interquartile range (IQR)102

Descriptive statistics

Standard deviation3188.4385
Coefficient of variation (CV)6.1443099
Kurtosis400.19208
Mean518.9254
Median Absolute Deviation (MAD)13
Skewness16.565025
Sum1.6620143 × 108
Variance10166140
MonotonicityNot monotonic
2023-03-08T14:30:03.838512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 52650
 
16.4%
2 25785
 
8.1%
3 16807
 
5.2%
4 12355
 
3.9%
5 9709
 
3.0%
6 8159
 
2.5%
7 6784
 
2.1%
8 5744
 
1.8%
9 5214
 
1.6%
10 4686
 
1.5%
Other values (10083) 172387
53.8%
ValueCountFrequency (%)
1 52650
16.4%
2 25785
8.1%
3 16807
 
5.2%
4 12355
 
3.9%
5 9709
 
3.0%
6 8159
 
2.5%
7 6784
 
2.1%
8 5744
 
1.8%
9 5214
 
1.6%
10 4686
 
1.5%
ValueCountFrequency (%)
165142 1
< 0.1%
158820 1
< 0.1%
155884 1
< 0.1%
154271 1
< 0.1%
154269 1
< 0.1%
144724 1
< 0.1%
118397 1
< 0.1%
115938 1
< 0.1%
111296 1
< 0.1%
110520 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct10857
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean613.09747
Minimum1
Maximum239960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:03.990403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3115
95-th percentile2014
Maximum239960
Range239959
Interquartile range (IQR)112

Descriptive statistics

Standard deviation4101.7374
Coefficient of variation (CV)6.6901881
Kurtosis715.7713
Mean613.09747
Median Absolute Deviation (MAD)14
Skewness21.19692
Sum1.9636286 × 108
Variance16824250
MonotonicityNot monotonic
2023-03-08T14:30:04.144162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 50678
 
15.8%
2 25350
 
7.9%
3 16633
 
5.2%
4 12141
 
3.8%
5 9634
 
3.0%
6 8124
 
2.5%
7 6788
 
2.1%
8 5688
 
1.8%
9 5147
 
1.6%
10 4680
 
1.5%
Other values (10847) 175417
54.8%
ValueCountFrequency (%)
1 50678
15.8%
2 25350
7.9%
3 16633
 
5.2%
4 12141
 
3.8%
5 9634
 
3.0%
6 8124
 
2.5%
7 6788
 
2.1%
8 5688
 
1.8%
9 5147
 
1.6%
10 4680
 
1.5%
ValueCountFrequency (%)
239960 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
231005 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226122 1
< 0.1%
223939 1
< 0.1%
218437 1
< 0.1%
215070 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

Distinct9048
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7781.1058
Minimum1
Maximum227968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:04.298102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42.95
Q1415
median1755
Q36516
95-th percentile37096
Maximum227968
Range227967
Interquartile range (IQR)6101

Descriptive statistics

Standard deviation17930.175
Coefficient of variation (CV)2.3043222
Kurtosis33.81119
Mean7781.1058
Median Absolute Deviation (MAD)1594
Skewness5.0340866
Sum2.4921326 × 109
Variance3.2149117 × 108
MonotonicityNot monotonic
2023-03-08T14:30:04.447855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 505
 
0.2%
25 479
 
0.1%
12 469
 
0.1%
8 453
 
0.1%
19 451
 
0.1%
14 442
 
0.1%
9 442
 
0.1%
17 435
 
0.1%
15 428
 
0.1%
26 424
 
0.1%
Other values (9038) 315752
98.6%
ValueCountFrequency (%)
1 350
0.1%
2 392
0.1%
3 368
0.1%
4 416
0.1%
5 376
0.1%
6 380
0.1%
7 385
0.1%
8 453
0.1%
9 442
0.1%
10 371
0.1%
ValueCountFrequency (%)
227968 23
< 0.1%
226768 23
< 0.1%
217958 24
< 0.1%
214514 17
< 0.1%
213536 25
< 0.1%
211593 17
< 0.1%
210433 19
< 0.1%
205348 17
< 0.1%
200603 16
< 0.1%
198527 20
< 0.1%
Distinct10080
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11211.894
Minimum1
Maximum369838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:04.596598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile54
Q1553
median2432
Q39252
95-th percentile52576
Maximum369838
Range369837
Interquartile range (IQR)8699

Descriptive statistics

Standard deviation26731.992
Coefficient of variation (CV)2.384253
Kurtosis37.468068
Mean11211.894
Median Absolute Deviation (MAD)2229
Skewness5.285701
Sum3.5909454 × 109
Variance7.145994 × 108
MonotonicityNot monotonic
2023-03-08T14:30:04.743959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 385
 
0.1%
24 368
 
0.1%
25 357
 
0.1%
38 353
 
0.1%
62 351
 
0.1%
11 349
 
0.1%
34 346
 
0.1%
32 342
 
0.1%
15 340
 
0.1%
6 339
 
0.1%
Other values (10070) 316750
98.9%
ValueCountFrequency (%)
1 270
0.1%
2 298
0.1%
3 302
0.1%
4 307
0.1%
5 325
0.1%
6 339
0.1%
7 304
0.1%
8 304
0.1%
9 258
0.1%
10 302
0.1%
ValueCountFrequency (%)
369838 23
< 0.1%
356486 23
< 0.1%
348523 25
< 0.1%
343318 24
< 0.1%
341692 19
< 0.1%
323791 20
< 0.1%
315783 17
< 0.1%
310778 17
< 0.1%
298646 17
< 0.1%
289045 16
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

Distinct297
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678394.5
Minimum1610
Maximum1487638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:04.906892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile43722
Q1300112
median757886
Q31026523
95-th percentile1340791
Maximum1487638
Range1486028
Interquartile range (IQR)726411

Descriptive statistics

Standard deviation409661.35
Coefficient of variation (CV)0.60386891
Kurtosis-1.0784094
Mean678394.5
Median Absolute Deviation (MAD)311287
Skewness-0.021183526
Sum2.1727619 × 1011
Variance1.6782242 × 1011
MonotonicityNot monotonic
2023-03-08T14:30:05.058100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880940 5102
 
1.6%
874114 4354
 
1.4%
843981 4347
 
1.4%
894326 4333
 
1.4%
880492 4273
 
1.3%
897718 4212
 
1.3%
764930 4089
 
1.3%
794553 4016
 
1.3%
1081202 3890
 
1.2%
1100282 3866
 
1.2%
Other values (287) 277798
86.7%
ValueCountFrequency (%)
1610 130
 
< 0.1%
1828 138
 
< 0.1%
1920 131
 
< 0.1%
2234 139
 
< 0.1%
2409 185
0.1%
2495 173
 
0.1%
6806 380
0.1%
11842 74
 
< 0.1%
17085 410
0.1%
18328 454
0.1%
ValueCountFrequency (%)
1487638 2975
0.9%
1450405 3048
1.0%
1421741 3564
1.1%
1344482 3543
1.1%
1340791 3441
1.1%
1332424 3545
1.1%
1316613 3463
1.1%
1282954 3576
1.1%
1265246 1177
 
0.4%
1262541 1201
 
0.4%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

Distinct297
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1094400.6
Minimum1861
Maximum2665627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:05.213615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile47348
Q1496620
median1088002
Q31728105
95-th percentile2549464
Maximum2665627
Range2663766
Interquartile range (IQR)1231485

Descriptive statistics

Standard deviation734398.49
Coefficient of variation (CV)0.6710509
Kurtosis-0.76547154
Mean1094400.6
Median Absolute Deviation (MAD)630818
Skewness0.35261249
Sum3.5051462 × 1011
Variance5.3934114 × 1011
MonotonicityNot monotonic
2023-03-08T14:30:05.368998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211793 5102
 
1.6%
1281514 4354
 
1.4%
1216262 4347
 
1.4%
1315592 4333
 
1.4%
1300459 4273
 
1.3%
1341876 4212
 
1.3%
1155560 4089
 
1.3%
1188880 4016
 
1.3%
2549464 3890
 
1.2%
2665627 3866
 
1.2%
Other values (287) 277798
86.7%
ValueCountFrequency (%)
1861 130
 
< 0.1%
2096 138
 
< 0.1%
2195 131
 
< 0.1%
2611 139
 
< 0.1%
2816 173
 
0.1%
3167 185
 
0.1%
7385 380
0.1%
12718 74
 
< 0.1%
20651 410
0.1%
23148 500
0.2%
ValueCountFrequency (%)
2665627 3866
1.2%
2620554 3790
1.2%
2620222 3788
1.2%
2594837 3844
1.2%
2549464 3890
1.2%
2481210 3851
1.2%
2179430 3757
1.2%
2062811 3811
1.2%
2052304 1168
 
0.4%
1990241 1167
 
0.4%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

Distinct3524
Distinct (%)1.3%
Missing48827
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean3575.003
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-03-08T14:30:05.522129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1480
median1255
Q34175
95-th percentile13455
Maximum287220
Range287150
Interquartile range (IQR)3695

Descriptive statistics

Standard deviation6513.1094
Coefficient of variation (CV)1.8218472
Kurtosis147.66205
Mean3575.003
Median Absolute Deviation (MAD)1025
Skewness7.2237192
Sum9.704453 × 108
Variance42420593
MonotonicityNot monotonic
2023-03-08T14:30:05.664613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2003
 
0.6%
105 1933
 
0.6%
110 1791
 
0.6%
185 1565
 
0.5%
180 1498
 
0.5%
300 1418
 
0.4%
145 1385
 
0.4%
175 1380
 
0.4%
120 1335
 
0.4%
165 1271
 
0.4%
Other values (3514) 255874
79.9%
(Missing) 48827
 
15.2%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 363
 
0.1%
85 918
0.3%
90 689
 
0.2%
95 695
 
0.2%
100 924
0.3%
105 1933
0.6%
110 1791
0.6%
115 1024
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116805 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
96890 5
< 0.1%

Interactions

2023-03-08T14:29:58.378780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:45.988107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:47.935543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:49.390932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:50.810950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:52.394163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:53.818919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:55.451425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:56.978832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:58.542370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:46.176374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:48.095536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:49.554629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:50.991218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:52.553603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:53.993405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:55.628737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:57.143121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:58.690910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:46.533314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:48.244523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:49.706753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:51.216051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:52.706361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:54.150819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:55.796935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:57.298291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:58.844167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:46.745786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:48.396888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:49.861830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:51.445754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:52.873271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:54.312495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:55.975346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:57.457855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:58.992015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:46.941245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:48.548664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:50.011545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:51.609069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:53.028884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:54.615486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:56.154751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:57.612303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:59.141845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:47.154664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:48.710598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:50.159109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:51.759783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:53.186648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:54.780635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:56.322498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:57.756566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:59.376771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:47.347648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:48.895110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:50.321030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:51.933376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:53.352714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:54.945861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:56.495088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:57.919256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:59.570853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:47.579991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:49.068276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:50.486564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:52.090302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:53.511076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:55.127322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:56.659697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:58.072656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:59.739698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:47.762990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:49.230947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:50.641405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:52.236657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:53.661217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:55.285582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:56.812797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T14:29:58.221902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-08T14:30:05.911883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
BEHANDELEND_SPECIALISME_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
BEHANDELEND_SPECIALISME_CD1.0000.2180.0080.013-0.060-0.054-0.556-0.4740.050
ZORGPRODUCT_CD0.2181.000-0.142-0.150-0.180-0.212-0.383-0.4110.026
AANTAL_PAT_PER_ZPD0.008-0.1421.0000.9960.3230.3210.0700.080-0.303
AANTAL_SUBTRAJECT_PER_ZPD0.013-0.1500.9961.0000.3190.3210.0730.087-0.306
AANTAL_PAT_PER_DIAG-0.060-0.1800.3230.3191.0000.9880.3150.2950.024
AANTAL_SUBTRAJECT_PER_DIAG-0.054-0.2120.3210.3210.9881.0000.3290.3260.033
AANTAL_PAT_PER_SPC-0.556-0.3830.0700.0730.3150.3291.0000.960-0.015
AANTAL_SUBTRAJECT_PER_SPC-0.474-0.4110.0800.0870.2950.3260.9601.000-0.017
GEMIDDELDE_VERKOOPPRIJS0.0500.026-0.303-0.3060.0240.033-0.015-0.0171.000

Missing values

2023-03-08T14:30:00.062005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-08T14:30:00.717927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-02-032023-02-012019-01-011900129919000061717177532286179304103932NaN
11.02023-02-032023-02-012019-01-0119001299190001112531286177532286179304103932170.0
21.02023-02-032023-02-012019-01-0119001299190000455177532286179304103932NaN
31.02023-02-032023-02-012019-01-0119001299190001610337117471775322861793041039321150.0
41.02023-02-032023-02-012019-01-01190012991900014309431091775322861793041039322085.0
51.02023-02-032023-02-012019-01-0119001299190001836494042177532286179304103932440.0
61.02023-02-032023-02-012019-01-0119001299190000825812655177532286179304103932460.0
71.02023-02-032023-02-012019-01-011900139919000222472173079304103932825.0
81.02023-02-032023-02-012019-01-0119001399190000369970272173079304103932475.0
91.02023-02-032023-02-012019-01-011900139919000262372173079304103932115.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3202701.02023-02-032023-02-012022-01-013061049903560602246563403184462224780.0
3202711.02023-02-032023-02-012022-01-01306104990356074114656340318446222585.0
3202721.02023-02-032023-02-012022-01-01306104179799007114656340318446222680.0
3202731.02023-02-032023-02-012022-01-0130610499035607636374656340318446222200.0
3202741.02023-02-032023-02-012022-01-013061049903560621146563403184462222005.0
3202751.02023-02-032023-02-012022-01-013061049903560592246563403184462222455.0
3202761.02023-02-032023-02-012022-01-0130610499035604822465634031844622211225.0
3202771.02023-02-032023-02-012022-01-01306104990356073224656340318446222535.0
3202781.02023-02-032023-02-012022-01-013061049903560561146563403184462227090.0
3202791.02023-02-032023-02-012022-01-01306104179799015574656340318446222150.0